People have been telling stories for thousands of years. What makes some more engaging?

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Morgan Chambers
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1 People have been telling stories for thousands of years What makes some more engaging?

2 Narrative Arcs and Cultural Success

4 Why do some cultural items succeed while others fail? Two possibilities

5 #1: Success is random Even domain experts have difficulty predicting success (Bielby & Bielby, 1994; Hirsch, 1972) Driven by patterns of social influence (Hahn & Bentley, 2003; Salganik, Dodds, & Watts, 2006)

6 #2: Individual level psychological processes shape collective outcomes Culture shapes psychological processes (Markus & Kitiyama, 1991). Reverse is also true: Psychological processes shape norms and practices

7 Cultural Selection Similar to biological notions (Dawkins 1976) Success depends on fit between item characteristics and shared human psychology Emotions, memory, etc. (Heath, Bell, & Sternberg 2001; Kashima 2008; Schaller & Crandall 2004)

8 Problem: How to quantify fit with people? Features of items? Solution: Natural Language Processing

9 Key Questions What best captures narrative trajectory? Emotional valence? Arousal? Something else? How to break up cultural item into chunks Scenes? Similar lengths? What features to capture? Micro-level/period-to-period -emotional volatility Macro-level - # of ups and downs and size

10 Original Star Wars

11 Emotions shape how we evaluate experiences Positive > negative Peak and end (Frederickson 2000; Redelmeier & Kahneman, 1996)

12 Less attention to emotional dynamics (Kuppens & Verduyn 2017) Particularly volatility 2 Low Volatility High Volatility

13 Volatility Should Increase Evaluation Volatility creates uncertainty and surprise which should increase emotional impact (Mellers, et al., 1997) For positive experiences (e.g., books and movies), this should boost evaluations Volatility should provide stimulation and reduce hedonic adaptation (Nelson and Meyvis 2008), both of which should lead positive experiences to be evaluated more favorably

14 Note about Volatility Focus on volatility between sizable chunks of a movie, like scenes, or portions of them. More granular (e.g., second-to-second) volatility less likely to leave an enduring impression and more likely to be measured with error Further, if a movie repeatedly oscillated back and forth between highly negative and positive in a matter of seconds, it would exhaust the viewer.

15 Methods 1) Collect scripts for thousands of movies 4125 movies from OpenSubtitles Score positivity/negativity of chunks of text Hedonometer (Dodds and Danforth 2010) 100 segments of 500 words 1) Plot emotional trajectory 2) Measure emotional volatility Std of difference between segments 3) Test relationship with success (IMDB ratings)

16 Results More emotionally volatile movies receive higher ratings (b = 9.40, s.e. = 0.788, p < 0.001) 10% increase in volatility linked to +1% in ratings

17 Robustness Movie length Genre Year of Release Amount of emotion some movies contain more emotion Peak and End Average Sentiment some movies are more pos or neg Complexity more characters or complex action Extremity - how much a trajectory diverges from the mean Results remain the same controlling for all these factors Alternate segment structures Number of segments (e.g., 50) Different length (e.g., 1000 words) Fixing overlap between segments (e.g., 1000 words, 900 overlap) No overlap between segments

18 Can we isolate impact of emotional volatility? Best approach would manipulate ONLY volatility, keep everything else the same Solution: movies with sequels

19 Isolating Emotional Volatility Movies in a series (N = 175) should perform better when they re more emotionally volatile Result: Even within the same series, more emotionally volatile movies are more successful (b = 8.86, s.e. = 4.32, p = 0.04) Originals effect? No. looking within sequels (2 nd vs. 3 rd movie), more emotionally volatile movies are more successful (b = 19.37, s.e. = 6.19, p = 0.04)

20 Variation by Genre If relationship is driven by surprise/stimulation, should be stronger in genres where those aspects contribute more to evaluation E.g., Thrillers and Mysteries

21 Macro features matter as well Take 100 word chunks Smooth the curve to drop small hitches Control for length (1) More runs (i.e., ups and downs), (2) Larger average runs, and (3) their interaction all positively linked to ratings How close are peaks? How do they change over time? Do they get progressively larger? More frequent?

22 Summary 1. Narrative arcs may help explain cultural success. 2. Individual-level psychological processes can shape collective outcomes (i.e., culture) 3. NLP can help link micro (psychological) and macro (cultural) processes.

23 Thank you.

25 Data Downloaded all movie scripts from OpenSubtitles Matched with IMDb.com profiles to acquire genre and user rating Remove movies with no match or very few words N = 4125 movies (Mean words = 7387, SD = 2558)

26 Calculate Emotional Volatility Use hedonometer (Dodds and Danforth 2010), to score sentiment of each word in the script Following prior work (Reagan et al 2016), focus on words with clear emotional content (i.e., 6 or 4) Divide movies into 100 segments of 500 words Results are robust to different segment numbers and sizes Calculate emotional score for each segment Volatility = standard deviation of difference between segments Simple stdof emotional scores doesn t indicate if change occurs

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